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Objective: The aim of this study was to compare the variances in-game loads exhibited by Chinese women’s 3 × 3 basketball team across different stages of the preparation cycle for the Tokyo Olympic Games, and to summarize the fundamental regulations governing Chinese women’s 3 × 3 basketball training and games, in order to establish a theoretical research foundation for the team’s new preparation cycle.Methods: This study measured load-related data during the preparation and main competition periods of the 2019–2021 Tokyo Olympics, from April to August 2019 and from April to June 2021. The aim was to compare the changes and differences in a load of competition during different stages and to explore patterns of load changes during the preparation period. This study used wearable devices authorized by FIFA and NBA, along with the Catapult GPS performance monitoring system from Australia (Catapult&Polar Team) as instruments for collecting sports load data. The OptimEye S5 device was worn around the athlete’s neck to collect data prior to the game, while the Open Field™ system was utilized for data editing and report generation post-game.Results: Compared to the primary competition load during the 2019 preparation period, the 2021 preparation period exhibited significant increases and decreases (p < 0.05) in competition load, high-intensity load, the number of explosive moves, the number of high-intensity acceleration, several changes to the left and right, and the number of explosive jumps. During the 2021 preparation period, the mean heart rate, mean heart rate percentage, and mean speed of the race demonstrated significant decreases in comparison to the race during the 2019 preparation period (p < 0.05). Throughout the training period spanning from 2019 to 2021, no significant differences were observed in running distance and maximum speed (p > 0.05).Conclusion: The findings of this study reveal that the national training team has fostered positive adaptive changes in athletes, resulting in a significant enhancement in both load and sports performance science data during competition from 2019 to 2021.

Closed-loop management of athletes at the training base is a compromise approach that balanced epidemic prevention and sports training during the COVID-19 pandemic. This study investigated the impact of prolonged closed-loop management on athletes’ sleep and mood during the 2022 Shanghai Omicron wave. The Pittsburgh Sleep Quality Index and the Profile of Mood States were used to assess the sleep and mood states of 110 professional athletes in "closed-loop management" at the training base after 1 and 2 months of closed-loop management, respectively, to characterize changes in sleep and mood with prolonged closed-loop management. After two months of control, the sleep and mood of 69 athletes and students of the same age were measured using the Pittsburgh Sleep Quality Index and Perceptual Stress Scale, as well as the Warwick-Edinburgh Mental Well-being Scale, to compare the differences in sleep and mood between athletes undergoing closed-loop management and the general population who were managed in the community. Paired sample t-tests and independent sample t-tests were used for comparisons across different time intervals and different management approaches. Results showed that with the time of closed-loop management increased, athletes woke up earlier (p = 0.002), slept less (p = 0.024), and became angrier (p = 0.014); athletes had poorer overall sleep quality (p < 0.001) but lower stress level (p = 0.004) than those who were outside the base. In closed-loop management, the athletes were able to maintain a stable sleep and mood state. Sports team administrators must be aware of the need to improve athletes’ sleep quality and help athletes to agree with this approach of management.

Objective Without appropriate training stimulus, the athlete experiences a loss of the physiological adaptations brought about by exercise. In most of highly trained athletes, short of training induces a rapid decline in VO2max, but it remains above control values. However, there is no specific information for normal people about the effects of detraining during certain period aerobic training. Therefore, the purpose of this study was to examine the effects of detraining between high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on myocardial AMPK and PGC-1α expression characterization time-sequential changes in Wistar rats, as well as the changing characteristics between AMPK and PGC-1α expression characterization and cardiacrespiratory fitness (CRF). Moreover, the potential mechanism for exercise arrangement was also investigated. Methods In this study, 27 four-month-old male Wistar rats were randomly divided into the sedentary control group (C), MICT group (M) and HIIT group (H).Animals in the training groups ran on a treadmill 5 days/week during 6 weeks. HIIT group (70%-90%-50%VO2max) and MICT (50%VO2max) group was ran for 50min exercise every training day. All the rats free to gather the food and drinking water. All rats were measured the VO2max after a week adaptive training and then the M group and H group began to exercise intervention. After 6 weeks of training, rats were randomly selected from each group at the 24h, the 3rd day, the 7th day and the 10th day. The Maximal oxygen uptake test was carried out before the samples were taken, and the abdominal aortic blood, myocardium and other tissues were collected after anesthesia. The expression characterization of AMPK and PGC-1α was tested by Western blotting analysis. All statistical analyses were performed using SPSS 17.0 and GraphPad Prism 5.01 for Windows. Data was presented as mean and standard deviation（SD), unless otherwise stated. The two-way ANOVA (intervention × time) with repeated measures were used to analyze differences of HIIT and MICT with time-sequential. One-way ANOVA was used to compare the difference between time-sequential among the groups for each variable. The relationship between variables was assessed using the Pearson correlation coefficient. The expression characterization of the detraining effect was also assessed using Cohen’s d effect sizes (ES) and thresholds (<0.5=small; 0.5~0.79=moderate;≥0.8=large). The level of significance was set at P<0.05 and the confidence intervals at 95%. Results VO2max showed a gradual downward trend in both H and M groups throughout the 10 days detraining periods. Detraining in the 10th day, training cessation resulted in the VO2max of H and M group were significantly lower than detraining 24h. (P<0.05). Detraining in 3rd day, myocardial AMPK and PGC-1α increased in H group, it was significantly higher than the C group (P<0.05), but there is no differences in the other detraining days(P>0.05). Furthermore, detraining in the 7th day myocardial PGC-1α decreased in H group, this value was significantly lower than detraining 24h (P<0.05). Detraining in 7th day, myocardial AMPK and PGC-1α started decreasing, but it was not significant than C group or other detraining days (P>0.05). Conclusions (1)The present data suggest that 6 weeks HIIT and MICT can increase the expression of myocardial AMPK and PGC-1α, the VO2max training effects disappeared after 10 days detraining. (2)Detraining during the 3rd day and the 7th day was the critical time point for retraining, endurance training intervention should be arranging among these days. (3)The VO2max time-sequential changes was partially consistent with AMPK and PGC-1α expression characterization, but AMPK and PGC-1α expression characterization was more sensitive than VO2max.

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